MP13-07 NEXT-GENERATION LIQUID BIOPSIES USING EXTRACELLULAR VESICLE DETECTION BY NANOSCALE FLOW CYTOMETRY
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection & Screening I (MP13)1 Apr 2019MP13-07 NEXT-GENERATION LIQUID BIOPSIES USING EXTRACELLULAR VESICLE DETECTION BY NANOSCALE FLOW CYTOMETRY Fabrice Lucien-Matteoni*, Janice Gomes, Harmenjit Brar, Matthew Lowerison, Mario Cepeda, Vidhu Joshi, Yohan Kim, Paras Shah, Stephen Pautler, Nicholas Power, Haidong Dong, Stephen Boorjian, and Bradley Leibovich Fabrice Lucien-Matteoni*Fabrice Lucien-Matteoni* More articles by this author , Janice GomesJanice Gomes More articles by this author , Harmenjit BrarHarmenjit Brar More articles by this author , Matthew LowerisonMatthew Lowerison More articles by this author , Mario CepedaMario Cepeda More articles by this author , Vidhu JoshiVidhu Joshi More articles by this author , Yohan KimYohan Kim More articles by this author , Paras ShahParas Shah More articles by this author , Stephen PautlerStephen Pautler More articles by this author , Nicholas PowerNicholas Power More articles by this author , Haidong DongHaidong Dong More articles by this author , Stephen BoorjianStephen Boorjian More articles by this author , and Bradley LeibovichBradley Leibovich More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555220.43463.25AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Next-generation biomarkers are emerging as valuable tools to improve cancer diagnostics, disease stratification and treatment monitoring. Our group has developed an innovative “liquid biopsy” based on enumeration of submicron cell fragments called extracellular vesicles (EVs) that bear tissue and cancer-specific biomarkers. This approach relies on the use of nanoscale flow cytometry (nFC) allowing high-throughput multi-parametric detection and enumeration of particles of events between 100-1000 nm in diameter. Despite the growing interest for developing EV-based blood tests, there is still an unmet need to optimize pre-analytical procedures and analytical parameters. Our team has established the standard operating procedures required for accurate detection of EVs from patient plasmas. METHODS: We utilized the A50-Micro Plus nanoscale flow cytometer (Apogee FlowSystems Inc.) to identify and measure 100-1000nm sized EVs. Silica and polystyrene beads were used to determine resolution limits of the nFC and established optimal acquisition parameters for EV enumeration. Plasmas from healthy volunteers and cancer patients were used to standardize pre-analytical conditions (plasma isolation, storage, handling) and to assess the performance of the nFC. RESULTS: A50-Micro Plus was capable of detecting EVs from 110 to 1000 nm in a linear manner by using light-scatter and fluorescence detection. Platelet-free plasma, storage temperature (-80C), dilution range (1/15-1/60) are critical considerations to ensure integrity and accurate enumeration of EVs. We used the standard operating procedure to enumerate prostate cancer-derived EVs in prostate cancer patients and unveiled a blood signature which identifies patients with clinically significant prostate cancers. CONCLUSIONS: We have established a workflow to develop EV-based liquid biopsies using nanoscale flow cytometry. In prostate cancer, we have identified an EV signature that may enhance selective identification of patients with clinically significant prostate cancer and decrease unnecessary tissue biopsies in individuals with absent or low-risk disease. This technique has the potential to facilitate the development of next-generation peripheral blood tests to allow personalized treatment protocols for patients with urogenital cancers. Source of Funding: Movember Foundation, Mayo Clinic Rochester, MN; London, Canada; Rochester, MN; London, Canada; Rochester, MN© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e179-e179 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Fabrice Lucien-Matteoni* More articles by this author Janice Gomes More articles by this author Harmenjit Brar More articles by this author Matthew Lowerison More articles by this author Mario Cepeda More articles by this author Vidhu Joshi More articles by this author Yohan Kim More articles by this author Paras Shah More articles by this author Stephen Pautler More articles by this author Nicholas Power More articles by this author Haidong Dong More articles by this author Stephen Boorjian More articles by this author Bradley Leibovich More articles by this author Expand All Advertisement PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".